Forecasting and Data Mining

The main difference between forecasting and data mining is on the goal of the task. The goal of forecasting is to make statements about the future, while the goal of data mining is to extract patterns from large datasets. (The term "data mining" was a buzzword 15 years ago to broadly refer to working on the data, which is a misuse.) Many techniques can be applied to both forecasting and data mining, such as artificial neural networks, regression analysis, and clustering analysis, and so forth.

Who's #1? Rating, Ranking and Provisional Leaderboard of GEFCom2014

The 12 evaluation weeks of GEFCom2014 just went by. Many contestants are curious to know about their rankings after such a marathon-type forecasting competition. Last weekend, I created a provisional leaderboard based on the scores I have documented on Inside Leaderboard. In this post, I will share this provisional leaderboard together with the rating and ranking methodology.

Again, this is not the final leaderboard, pending corrections of individual scores (if there were errors) and adjustment of rankings based on the final reports. 

Documentation in Load Forecasting: 4 Reasons and 8 Elements

In load forecasting, especially long term load forecasting, documentation is probably the most important task. The ultimate test of documentation quality is whether the forecasting system has been described in detail so that other people with relevant education background and experience can reproduce the forecasts.

While forecasting is like an adventure, exploring an unknown trail, documentation is like walking the same trail again and again to record what happened in detail. Documentation often requires significant amount of efforts, sometimes more than forecasting itself.

Inside Leaderboard

Update 11/22/2014:

Include a column showing the teams ineligible for the final leaderboard due to missing more than three submissions beating benchmark. 

The first evaluation week (Task 4) of GEFCom2014 just went by. CrowdAnalytix publishes the leaderboard based on the best score of each team in real time. Since some teams made multiple submissions, that leaderboard won't reflect the real positions. To enhance the transparency of our scoring process, I manually pulled the submission log of each track to come up with a more realistic leaderboard. Please understand that:

Towards Winning GEFCom2014 - Six Must Read Recommendations before Evaluation Period Starts

GEFCom2014 had a super strong start. Within the first 4 weeks, we have had 225, 138, 126 and 135 solvers in the load, price, wind and solar tracks respectively. Totally 345 people have joined the LinkedIn group Global Energy Forecasting Competition. While many winning teams of GEFCom2012 came back to GEFCom2014 with very strong performance, several new faces also topped some of the tasks. Having been monitoring the competition from the back end, I am so excited about the ups and downs on the leaderboards. I really wish I could join the game in person.

The evaluation period of GEFCom2014 is starting in less than 2 days. I'd like to offer some recommendations, so that the contestants fully understand what makes a winning solution. Some of them may be overlapping with my previous post, GEFCom2014 is ON - 8 Tips before You Join the Game, but I think it's important to cover them again here.

GEFCom2014 is ON - 8 Tips before You Join the Game

After one full year of planning and implementation, I'm pleased to announce that the Global Energy Forecasting Competition 2014 is ON. Please visit CrowdANALYTIX.com to look for the four tracks.

For now, I will defer all the thanks to the end of the competition. (BTW, it will have to be a long thank-you letter, because so many people have devoted so many days and nights to set up this competition.) Instead, I would spend my last sleepless night before the competition to provide a few tips and instructions to the GEFCom2014 contestants:

10 Recommended Papers for GEFCom2014 Contestants

Update 9/20/2014:

Rafal Weron's review on price forecasting is available on Science Direct with open access. The probabilistic load forecasting review paper written by Shu Fan and myself is currently under review by IJF. The link to the working paper is listed below.

Update 8/14/2014:

Thanks to Rob Hyndman, who generously put Rafal Weron's forthcoming IJF paper on the web. Now we have a super well-written review paper on price forecasting on the list as Ref [11]. This paper will be published in the coming issue of International Journal of Forecasting.

The Global Energy Forecasting Competition 2014 (GEFCom2014) is the first probabilistic forecasting competition in the power and energy industry. As of today, over 200 people from more than 40 countries have signed up the interest list. To help the folks quickly get use to the theme of this competition, we organizers collaboratively picked up 10 recommended papers for GEFCom2014 contestants.

Ref [1-3] are general readings on probabilistic forecasting, energy forecasting and the previous competition respectively. Ref [4-6], Ref [7-8] and Ref [9-10] are for load, wind and solar forecasting respectively. We did not recommend any specific paper on probabilistic price forecasting. The contestants on the price forecasting track may refer to the recommended papers on load and wind forecasting. The literature on probabilistic wind power forecasting is dominantly more extensive and mature than the other three categories. Therefore, we would highly recommend the contestants to read Ref [7-8] regardless which track to work on. Shu and I are preparing the tutorial review on probabilistic load forecasting (Ref [4]), which will serve as a general guideline as well.

What's New in Energy Forecasting - Jun 2014

There are four exciting events going on in the energy forecasting community:

1. Global Energy Forecasting Competition 2014
We will launch GEFCom2014 in August. This competition will feature four tracks: electric load forecasting, electricity price forecasting, wind power forecasting and solar power forecasting. All tracks will be under the theme of probabilistic energy forecasting. The competition will last for 15 weeks with incremental data update every week for rolling forecast. The competition forum is on LinkedIn. For more information, please visit www.gefcom.org.

2. Activities at 2014 IEEE PES General Meeting
IEEE Working Group on Energy Forecasting is hosting a one-day tutorial and a panel session at PESGM2014:

• Energy Forecasting in the Smart Grid Era (tutorial) Sunday, 27 July, 2014 8:00 AM-5:00 PM Chesapeake G
• Load Forecasting: the State of the Practice (panel session) Monday, 28 July, 2014 2:00 PM-5:00 PM National Harbor 4

3. Special Issue on Probabilistic Energy Forecasting | International Journal of Forecasting
Although the abstract submission deadline has passed (refer to Call For Papers), we welcome submissions of high quality work on this topic. In addition, the winners of GEFCom2014 will be invited to submit their methods to this special issue.

4. International Symposium on Forecasting
International Institute of Forecasters is hosting the 34th International Symposium on Forecasting in Rotterdam, The Netherlands, 6/29-7/2, 2014. There are four sessions on energy forecasting: Probabilistic Energy Forecasting; Electricity Demand I; Electricity Demand II; and Oil Prices. The abstracts of the talks are available in the program book. The full conference proceeding with presentations and papers will be available on the conference website. Stay tuned.

A Preview of Global Energy Forecasting Competition 2014

The news article below is what I wrote for The Oracle published by the International Institute of Forecasters. It will show up in the March 2014 issue.

Energy forecasting, in a broad sense, covers a wide range of forecasting problems in the energy industry, such as demand forecasting, generation forecasting, price forecasting and so on. These problems are very attractive to the forecasting community due to the characteristics including high resolution data, multiple seasonality, requirements of low errors and societal necessity.

GEFCom2012 Papers

Thanks to International Journal of Forecasting and IEEE Transactions on Smart Grid, our two publication sponsors of GEFCom2012, all of the invited GEFCom2012 papers have been published as of today. These papers, in many aspects, represent the state-of-the-art in load and wind forecasting. The citations in IEEE format and links to these papers are provided below.

Announcing Institute Prize for Global Energy Forecasting Competition 2014

To encourage students and faculty participation, we will recognize up to three academic institutes for the excellent performance in the Global Energy Forecasting Competition 2014 (GEFCom2014).

Each individual team that beats the benchmark and ranks top 8 of a track will receive 10 - r points, where r is the ranking of the team. The institute score is the sum of points from all teams associated with the institute. The top three institutes will be recognized with award plaques. The one with the highest score will be awarded the institute price of $500 for the best institute performance. The primary faculty overseeing the participating teams will claim the prize and/or recognition on behalf of a winning institute.

The following terms and conditions apply:

• A team participating and ranking top 8 in multiple tracks will receive the points equal to the sum of points from each track.
• A winning institute has to have at least two teams beating the benchmark(s) and at least one top 8 team. 
• The primary faculty should register all team members prior to the registration deadline. 
• If two institutes receive the same scores, the one with more top 8 teams will have a higher ranking. The priority then goes to the one with more teams beating the benchmark(s).
• If no institute fulfills the above requirements, GEFCom2014 will not award the institute prize. 
• The GEFCom2014 executive committee reserve the rights to interpret and revise the competition rules and other related information.

Guest Editorial: Special Section on Analytics for Energy Forecasting with Applications to Smart Grid

I'm glad to announce that the special section we edited last year has been published by IEEE Transactions on Smart Grid. I had a great pleasure working with and learning from my guest editors during this editorial process. The guest editorial is available on IEEE Xplore with open access.

IEEE Transactions on Smart Grid, vol.5, no.1, pp. 399-401, Jan 2014

Guest Editorial: Special Section on Analytics for Energy Forecasting with Applications to Smart Grid 

Tao Hong, Shu Fan, Wei-Jen Lee, Wenyuan Li, Anil Pahwa, Pierre Pinson, Jianhui Wang and Hamidreza Zareipour

Thanks to the help from the reviewers and editors, totally 15 high quality forecasting papers are included in this special section, which can be categorized into 5 groups:

1. Global Energy Forecasting Competition;
2. Load forecasting and analysis with high granular data;
3. Probabilistic energy forecasting;
4. Forecasting and analysis of emerging subjects;
5. Novel methods for wind power forecasting.

The links to the papers with the IEEE citation format are provided below with indicators of the groups they fall into:

• Y. Goude, R. Nedellec, and N. Kong, "Local short and middle term electricity load forecasting with semi-parametric additive models," IEEE Trans. Smart Grid, vol. 5, pp. 440-446, Jan. 2014. (1)
• D. Lee, and R. Baldick, "Short-Term Wind Power Ensemble Prediction Based on Gaussian Processes and Neural Networks," IEEE Trans. Smart Grid, vol. 5, pp. 501-510, Jan. 2014. (1, 5)
• M. Chaouch, "Clustering-based improvement of nonparametric functional time series forecasting: application to intra-day household-level load curves," IEEE Trans. Smart Grid, vol. 5, pp. 411-419, Jan. 2014. (2)
• J. Kwac, J. Flora, and R. Rajagopal, "Household energy consumption segmentation using hourly data," IEEE Trans. Smart Grid, vol. 5, pp. 420-430, Jan. 2014. (2)
• P. G. D. Silva, D. Ilić, and S. Karnouskos, "The impact of smart grid prosumer grouping on forecasting accuracy and its benefits for local electricity market trading," IEEE Trans. Smart Grid, vol. 5, pp. 402-410, 2014. (2)
• J. D. Black, and W. L. W. Henson, "Hierarchical load hindcasting using reanalysis weather," IEEE Trans. Smart Grid, vol. 5, pp. 447-455, Jan. 2014. (2)
• T. Hong, J. Wilson, and J. Xie, "Long term probabilistic load forecasting and normalization with hourly information," IEEE Trans. Smart Grid, vol. 5, pp. 456-462, Jan. 2014. (2, 3)
• J. Tastu, P. Pinson, P. Trombe, and H. Madsen, "Probabilistic forecasts of wind power generation accounting for geographically dispersed information," IEEE Trans. Smart Grid, vol. 5, pp. 480-489, Jan. 2014. (3, 5)
• D. Lee, and R. Baldick, "Future wind power scenario synthesis through power spectral density analysis," IEEE Trans. Smart Grid, vol. 5, pp. 490-500, Jan. 2014. (3, 5)
• C. Wan, Z. Xu, Y. Wang, Z. Y. Dong, and S. K. P. Wong, "A hybrid approach for probabilistic forecasting of electricity price," IEEE Trans. Smart Grid, vol. 5, pp. 463-470, Jan. 2014. (3)
• Z. Wang, M. Begovic, and J. Wang, "Analysis of conservation voltage reduction effects based on multistage SVR and stochastic process," IEEE Trans. Smart Grid, vol. 5, pp. 431-439, Jan. 2014. (2, 4)
• P. Wang, H. Zareipour, and W. D. Rosehart, "Descriptive models for reserve and regulation prices in competitive electricity markets," IEEE Trans. Smart Grid, vol. 5, pp. 471-479, Jan. 2014. (4)
• Z. Duan, B. Gutierrez, and L. Wang, "Forecasting plug-In electric vehicle sales and the diurnal recharging load curve," IEEE Trans. Smart Grid, vol. 5, pp. 527-535, Jan. 2014. (4)
• J. Shi, Z. Ding, W. Lee, Y. Yang, Y. Liu, and M. Zhang, "Hybrid forecasting model for very-short term wind power forecasting based on grey relational analysis and wind speed distribution features," IEEE Trans. Smart Grid, vol. 5, pp. 521-526, Jan. 2014. (5)
• L. Xie, Y. Gu, X. Zhu, and M. G. Genton, "Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch," IEEE Trans. Smart Grid, vol. 5, pp. 511-520, Jan. 2014. (5)

Enjoy reading!

Publication Sponsors Announced for Global Energy Forecasting Competition 2014

The success of the Global Energy Forecasting Competition has been broadcasted by major media outlets, such as Yahoo Finance, CNBC, and IEEE PES. Last year, we were very fortunate to have International Journal of Forecasting (IJF) and IEEE Transactions on Smart Grid as our publication sponsors, which greatly contribute to the success. The papers discussing the winning methods used in GEFCom2012 are now on the journals' websites. The complete set of competition data is published with the introduction paper "Global Energy Forecasting Competition 2012", http://dx.doi.org/10.1016/j.ijforecast.2013.07.001.
I'm pleased to announce that we have two prestigious journals sponsoring our next competition GEFCom2014: International Journal of Forecasting and Foresight: The International Journal of Applied Forecasting (Foresight). A special issue of IJF will be dedicated to Probabilistic Energy Forecasting (Call For Papers: Probabilistic Energy Forecasting), which will archive the papers from the winning teams of GEFCom2014.
If you are interested in GEFCom2014, please join the interest list HERE.

Global Energy Forecasting Competition 2012: Lessons Learned and Beyond

In 2012, IEEE Working Group on Energy Forecasting organized the Global Energy Forecasting Competition (GEFCom2012). The competition, which included two tracks, Hierarchical Load Forecasting and Wind Power Forecasting, attracted thousands of data scientists from over 30 countries (see the map below). The 8 winning teams were formulated by people from 8 countries. The competition brought together many novel ideas that are meaningful to the improvement of the forecasting practices in the utility industry. In this webinar, I will present the lessons learned from GEFCom2012, which include several key findings and insights based on the approaches taken by the winning entries of both tracks. In addition, I will offer a preview of the future upgrades in GEFCom2014.

If you are interested, please go to the webinar page to register. The presentation slides are available through the webinar page as well.
Please notice that this webinar is co-sponsored by SAS Utilities User Group. Therefore, it will be the only webinar on my webinar series so far with the audio recording available.

Winners Announced for Global Energy Forecasting Competition 2012

Two weeks ago at 2013 PES General Meeting held in Vancouver, BC, we announced the 8 winning teams of Global Energy Forecasting Competition (GEFCom2012). The picture below was taken at the end of the award reception.

I'm delighted to post the winners here:

What's New in Energy Forecasting - Jun 2013

There are three exciting events going on in the energy forecasting community:
1. Global Energy Forecasting Competition
GEFCom2012 has been a great success. A valuable benchmark data pool is being established through this competition. Many novel ideas are brought to the field by the data scientists worldwide. Several papers from the top entries will be published in two prestigious scholarly journals, International Journal of Forecasting and IEEE Transactions on Smart Grid. An overview paper can be accessed from this post.
Meanwhile, we have started planning GEFCom2014. If you are interested, you are welcome to join the interest list to get the most recent update.

Global Energy Forecasting Competition 2012

The paper is available on Science Direct.

Citation
Tao Hong, Pierre Pinson and Shu Fan, "Global Energy Forecasting Competition 2012", International Journal of Forecasting, vol.30, no.2., pp 357-363, April - June, 2014

Global Energy Forecasting Competition 2012

Tao Hong, Pierre Pinson and Shu Fan

Abstract

The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details on the aspects of the problem, the data, and a summary of the methods used by selected top entries. We also discuss the lessons learned from this competition from the organizers’ perspective. The complete data set, including the solution data, is published along with this paper, in an effort to establish a benchmark data pool for the community.

Post-GEFCom2012 Activities

There are several activities following up GEFCom2012:

1. Nine finalists will present their work at IEEE PES General Meeting 2013. Four of them are from the hierarchical load forecasting track: Colin Singleton from CountingLab Ltd, James Robert Lloyd from University of Cambridge, Raphael Nédellec from EDF R&D, and Souhaib Ben Taieb from Université Libre de Bruxelles. Five of them are from the wind power forecasting track: Lucas Eustaquio from DTI Sistemas, Ekaterina Mangalova from Siberian State Aerospace University, Matt Wytock from Carnegie Mellon University, Gabor I. Nagy from Budapset University of Technology and Economics, and Duehee Lee from University of Texas at Austin.

2. International Journal of Forecasting (IJF) will publish a few short papers from GEFCom2012, including one introduction paper and several other papers describing the methodologies used by the top teams. The competition data including the results will be published with the introduction paper.

3. IEEE Transactions of Smart Grid will publish a few full papers from GEFCom2012 in the Special Issue on Analytics for Energy Forecasting with Applications to Smart Grid.

Congratulations, GEFCom2012 Contestants!

Thank Kaggle's data scientists, the Advisory Committee and the officers of GEFCom2012, and of course hundreds of contestants who made this competition a fantastic and exciting event! While we are still accepting reports from the contestants for final evaluation, I can't wait to share some summary statistics with you all. There are two tracks in GEFCom2012, hierarchical load forecasting and wind power forecasting. During the planning stage, we have received over 100 sign-ups across more than 30 countries in total. As of today...

More about GEFCom2012

Up to date, 100+ people from the following 31 countries have filled in the Call For Participants survey: Australia, Belgium, Brazil, Canada, China, Croatia, Cyprus, Denmark, France, Germany, Greece, Hungary, India, Iran, Italy, Japan, Mexico, Netherlands, New Zealand, Norway, Pakistan, Poland, Russia, Singapore, South Africa, Spain, Sweden, Switzerland, Turkey, UK, US While Kaggle is setting up the online competition environment for us, here are some more details about the competition: